  np.mean() vs np.average() in Python NumPy?

mean | StackOverflow

I notice that

In : np.mean([1, 2, 3])
Out: 2.0

In : np.average([1, 2, 3])
Out: 2.0

However, there should be some differences, since after all they are two different functions.

What are the differences between them?

np.average takes an optional weight parameter. If it is not supplied they are equivalent. Take a look at the source code: Mean, Average

np.mean:

try:
mean = a.mean
except AttributeError:
return _wrapit(a, "mean", axis, dtype, out)
return mean(axis, dtype, out)

np.average:

...
if weights is None :
avg = a.mean(axis)
scl = avg.dtype.type(a.size/avg.size)
else:
#code that does weighted mean here

if returned: #returned is another optional argument
scl = np.multiply(avg, 0) + scl
return avg, scl
else:
return avg
...

Meaning of @classmethod and @staticmethod for beginner?

Question by user1632861

Could someone explain to me the meaning of @classmethod and @staticmethod in python? I need to know the difference and the meaning.

As far as I understand, @classmethod tells a class that it"s a method which should be inherited into subclasses, or... something. However, what"s the point of that? Why not just define the class method without adding @classmethod or @staticmethod or any @ definitions?

tl;dr: when should I use them, why should I use them, and how should I use them?

What is the meaning of single and double underscore before an object name?

Can someone please explain the exact meaning of having single and double leading underscores before an object"s name in Python, and the difference between both?

Also, does that meaning stay the same regardless of whether the object in question is a variable, a function, a method, etc.?

What does -> mean in Python function definitions?

I"ve recently noticed something interesting when looking at Python 3.3 grammar specification:

funcdef: "def" NAME parameters ["->" test] ":" suite

The optional "arrow" block was absent in Python 2 and I couldn"t find any information regarding its meaning in Python 3. It turns out this is correct Python and it"s accepted by the interpreter:

def f(x) -> 123:
return x

I thought that this might be some kind of a precondition syntax, but:

• I cannot test x here, as it is still undefined,
• No matter what I put after the arrow (e.g. 2 < 1), it doesn"t affect the function behavior.

Could anyone accustomed with this syntax style explain it?

What does the star and doublestar operator mean in a function call?

What does the * operator mean in Python, such as in code like zip(*x) or f(**k)?

1. How is it handled internally in the interpreter?
2. Does it affect performance at all? Is it fast or slow?
3. When is it useful and when is it not?
4. Should it be used in a function declaration or in a call?

Get statistics for each group (such as count, mean, etc) using pandas GroupBy?

I have a data frame df and I use several columns from it to groupby:

df["col1","col2","col3","col4"].groupby(["col1","col2"]).mean()

In the above way I almost get the table (data frame) that I need. What is missing is an additional column that contains number of rows in each group. In other words, I have mean but I also would like to know how many number were used to get these means. For example in the first group there are 8 values and in the second one 10 and so on.

In short: How do I get group-wise statistics for a dataframe?

What does -1 mean in numpy reshape?

A numpy matrix can be reshaped into a vector using reshape function with parameter -1. But I don"t know what -1 means here.

For example:

a = numpy.matrix([[1, 2, 3, 4], [5, 6, 7, 8]])
b = numpy.reshape(a, -1)

The result of b is: matrix([[1, 2, 3, 4, 5, 6, 7, 8]])

Does anyone know what -1 means here? And it seems python assign -1 several meanings, such as: array[-1] means the last element. Can you give an explanation?

In Matplotlib, what does the argument mean in fig.add_subplot(111)?

Sometimes I come across code such as this:

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
fig = plt.figure()
plt.scatter(x, y)
plt.show()

Which produces: I"ve been reading the documentation like crazy but I can"t find an explanation for the 111. sometimes I see a 212.

What does the argument of fig.add_subplot() mean?

What does it mean if a Python object is "subscriptable" or not?

Question by Alistair

Which types of objects fall into the domain of "subscriptable"?

What does "SyntaxError: Missing parentheses in call to "print"" mean in Python?

When I try to use a print statement in Python, it gives me this error:

>>> print "Hello, World!"
File "<stdin>", line 1
print "Hello, World!"
^
SyntaxError: Missing parentheses in call to "print"

What does that mean?

What does `ValueError: cannot reindex from a duplicate axis` mean?

I am getting a ValueError: cannot reindex from a duplicate axis when I am trying to set an index to a certain value. I tried to reproduce this with a simple example, but I could not do it.

Here is my session inside of ipdb trace. I have a DataFrame with string index, and integer columns, float values. However when I try to create sum index for sum of all columns I am getting ValueError: cannot reindex from a duplicate axis error. I created a small DataFrame with the same characteristics, but was not able to reproduce the problem, what could I be missing?

I don"t really understand what ValueError: cannot reindex from a duplicate axismeans, what does this error message mean? Maybe this will help me diagnose the problem, and this is most answerable part of my question.

ipdb> type(affinity_matrix)
<class "pandas.core.frame.DataFrame">
ipdb> affinity_matrix.shape
(333, 10)
ipdb> affinity_matrix.columns
Int64Index([9315684, 9315597, 9316591, 9320520, 9321163, 9320615, 9321187, 9319487, 9319467, 9320484], dtype="int64")
ipdb> affinity_matrix.index
Index([u"001", u"002", u"003", u"004", u"005", u"008", u"009", u"010", u"011", u"014", u"015", u"016", u"018", u"020", u"021", u"022", u"024", u"025", u"026", u"027", u"028", u"029", u"030", u"032", u"033", u"034", u"035", u"036", u"039", u"040", u"041", u"042", u"043", u"044", u"045", u"047", u"047", u"048", u"050", u"053", u"054", u"055", u"056", u"057", u"058", u"059", u"060", u"061", u"062", u"063", u"065", u"067", u"068", u"069", u"070", u"071", u"072", u"073", u"074", u"075", u"076", u"077", u"078", u"080", u"082", u"083", u"084", u"085", u"086", u"089", u"090", u"091", u"092", u"093", u"094", u"095", u"096", u"097", u"098", u"100", u"101", u"103", u"104", u"105", u"106", u"107", u"108", u"109", u"110", u"111", u"112", u"113", u"114", u"115", u"116", u"117", u"118", u"119", u"121", u"122", ...], dtype="object")

ipdb> affinity_matrix.values.dtype
dtype("float64")
ipdb> "sums" in affinity_matrix.index
False

Here is the error:

ipdb> affinity_matrix.loc["sums"] = affinity_matrix.sum(axis=0)
*** ValueError: cannot reindex from a duplicate axis

I tried to reproduce this with a simple example, but I failed

In : import pandas as pd

In : import numpy as np

In : a = np.arange(35).reshape(5,7)

In : df = pd.DataFrame(a, ["x", "y", "u", "z", "w"], range(10, 17))

In : df.values.dtype
Out: dtype("int64")

In : df.loc["sums"] = df.sum(axis=0)

In : df
Out:
10  11  12  13  14  15   16
x      0   1   2   3   4   5    6
y      7   8   9  10  11  12   13
u     14  15  16  17  18  19   20
z     21  22  23  24  25  26   27
w     28  29  30  31  32  33   34
sums  70  75  80  85  90  95  100

Recommendation for beginners:

This is my personal recommendation for beginners: start by learning virtualenv and pip, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.

PyPI packages not in the standard library:

• virtualenv is a very popular tool that creates isolated Python environments for Python libraries. If you"re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I"ll be making comparisons to it for the rest of this answer.

It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). An exact copy of the python or python3 binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It"s not part of Python"s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using pip.

• pyenv is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you"ll need a way to switch between them. Once activated, it prefixes the PATH environment variable with ~/.pyenv/shims, where there are special files matching the Python commands (python, pip). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the PYENV_VERSION environment variable, or the .python-version file, or the ~/.pyenv/version file. pyenv also makes the process of downloading and installing multiple Python versions easier, using the command pyenv install.

• pyenv-virtualenv is a plugin for pyenv by the same author as pyenv, to allow you to use pyenv and virtualenv at the same time conveniently. However, if you"re using Python 3.3 or later, pyenv-virtualenv will try to run python -m venv if it is available, instead of virtualenv. You can use virtualenv and pyenv together without pyenv-virtualenv, if you don"t want the convenience features.

• virtualenvwrapper is a set of extensions to virtualenv (see docs). It gives you commands like mkvirtualenv, lssitepackages, and especially workon for switching between different virtualenv directories. This tool is especially useful if you want multiple virtualenv directories.

• pyenv-virtualenvwrapper is a plugin for pyenv by the same author as pyenv, to conveniently integrate virtualenvwrapper into pyenv.

• pipenv aims to combine Pipfile, pip and virtualenv into one command on the command-line. The virtualenv directory typically gets placed in ~/.local/share/virtualenvs/XXX, with XXX being a hash of the path of the project directory. This is different from virtualenv, where the directory is typically in the current working directory. pipenv is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to pipenv, such as poetry, which I won"t list here since this question is only about the packages that are similarly named.

Standard library:

• pyvenv (not to be confused with pyenv in the previous section) is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is python3 -m venv.

• venv is a package shipped with Python 3, which you can run using python3 -m venv (although for some reason some distros separate it out into a separate distro package, such as python3-venv on Ubuntu/Debian). It serves the same purpose as virtualenv, but only has a subset of its features (see a comparison here). virtualenv continues to be more popular than venv, especially since the former supports both Python 2 and 3.

You have four main options for converting types in pandas:

1. to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)

2. astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).

3. infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.

4. convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).

Read on for more detailed explanations and usage of each of these methods.

1. to_numeric()

The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().

This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.

Basic usage

The input to to_numeric() is a Series or a single column of a DataFrame.

>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0      8
1      6
2    7.5
3      3
4    0.9
dtype: object

>>> pd.to_numeric(s) # convert everything to float values
0    8.0
1    6.0
2    7.5
3    3.0
4    0.9
dtype: float64

As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:

# convert Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

You can also use it to convert multiple columns of a DataFrame via the apply() method:

# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame

# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)

As long as your values can all be converted, that"s probably all you need.

Error handling

But what if some values can"t be converted to a numeric type?

to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.

Here"s an example using a Series of strings s which has the object dtype:

>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0         1
1         2
2       4.7
3    pandas
4        10
dtype: object

The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":

>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string

Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:

>>> pd.to_numeric(s, errors="coerce")
0     1.0
1     2.0
2     4.7
3     NaN
4    10.0
dtype: float64

The third option for errors is just to ignore the operation if an invalid value is encountered:

>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched

This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:

df.apply(pd.to_numeric, errors="ignore")

The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.

Downcasting

By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).

That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?

to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

Downcasting to "integer" uses the smallest possible integer that can hold the values:

>>> pd.to_numeric(s, downcast="integer")
0    1
1    2
2   -7
dtype: int8

Downcasting to "float" similarly picks a smaller than normal floating type:

>>> pd.to_numeric(s, downcast="float")
0    1.0
1    2.0
2   -7.0
dtype: float32

2. astype()

The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.

Basic usage

Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).

Call the method on the object you want to convert and astype() will try and convert it for you:

# convert all DataFrame columns to the int64 dtype
df = df.astype(int)

# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})

# convert Series to float16 type
s = s.astype(np.float16)

# convert Series to Python strings
s = s.astype(str)

# convert Series to categorical type - see docs for more details
s = s.astype("category")

Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.

As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.

Be careful

astype() is powerful, but it will sometimes convert values "incorrectly". For example:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

These are small integers, so how about converting to an unsigned 8-bit type to save memory?

>>> s.astype(np.uint8)
0      1
1      2
2    249
dtype: uint8

The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!

Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.

3. infer_objects()

Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).

For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:

>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a    object
b    object
dtype: object

Using infer_objects(), you can change the type of column "a" to int64:

>>> df = df.infer_objects()
>>> df.dtypes
a     int64
b    object
dtype: object

Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.

4. convert_dtypes()

Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.

Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.

With our object DataFrame df, we get the following result:

>>> df.convert_dtypes().dtypes
a     Int64
b    string
dtype: object

Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).

Column "b" contained string objects, so was changed to pandas" string dtype.

By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:

>>> df.convert_dtypes(infer_objects=False).dtypes
a    object
b    string
dtype: object

Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.

How to iterate over rows in a DataFrame in Pandas?

Iteration in Pandas is an anti-pattern and is something you should only do when you have exhausted every other option. You should not use any function with "iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.

Do you want to print a DataFrame? Use DataFrame.to_string().

Do you want to compute something? In that case, search for methods in this order (list modified from here):

1. Vectorization
2. Cython routines
3. List Comprehensions (vanilla for loop)
4. DataFrame.apply(): i) ¬†Reductions that can be performed in Cython, ii) Iteration in Python space
5. DataFrame.itertuples() and iteritems()
6. DataFrame.iterrows()

iterrows and itertuples (both receiving many votes in answers to this question) should be used in very rare circumstances, such as generating row objects/nametuples for sequential processing, which is really the only thing these functions are useful for.

Appeal to Authority

The documentation page on iteration has a huge red warning box that says:

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed [...].

* It"s actually a little more complicated than "don"t". df.iterrows() is the correct answer to this question, but "vectorize your ops" is the better one. I will concede that there are circumstances where iteration cannot be avoided (for example, some operations where the result depends on the value computed for the previous row). However, it takes some familiarity with the library to know when. If you"re not sure whether you need an iterative solution, you probably don"t. PS: To know more about my rationale for writing this answer, skip to the very bottom.

Faster than Looping: Vectorization, Cython

A good number of basic operations and computations are "vectorised" by pandas (either through NumPy, or through Cythonized functions). This includes arithmetic, comparisons, (most) reductions, reshaping (such as pivoting), joins, and groupby operations. Look through the documentation on Essential Basic Functionality to find a suitable vectorised method for your problem.

If none exists, feel free to write your own using custom Cython extensions.

Next Best Thing: List Comprehensions*

List comprehensions should be your next port of call if 1) there is no vectorized solution available, 2) performance is important, but not important enough to go through the hassle of cythonizing your code, and 3) you"re trying to perform elementwise transformation on your code. There is a good amount of evidence to suggest that list comprehensions are sufficiently fast (and even sometimes faster) for many common Pandas tasks.

The formula is simple,

# Iterating over one column - `f` is some function that processes your data
result = [f(x) for x in df["col"]]
# Iterating over two columns, use `zip`
result = [f(x, y) for x, y in zip(df["col1"], df["col2"])]
# Iterating over multiple columns - same data type
result = [f(row, ..., row[n]) for row in df[["col1", ...,"coln"]].to_numpy()]
# Iterating over multiple columns - differing data type
result = [f(row, ..., row[n]) for row in zip(df["col1"], ..., df["coln"])]

If you can encapsulate your business logic into a function, you can use a list comprehension that calls it. You can make arbitrarily complex things work through the simplicity and speed of raw Python code.

Caveats

List comprehensions assume that your data is easy to work with - what that means is your data types are consistent and you don"t have NaNs, but this cannot always be guaranteed.

1. The first one is more obvious, but when dealing with NaNs, prefer in-built pandas methods if they exist (because they have much better corner-case handling logic), or ensure your business logic includes appropriate NaN handling logic.
2. When dealing with mixed data types you should iterate over zip(df["A"], df["B"], ...) instead of df[["A", "B"]].to_numpy() as the latter implicitly upcasts data to the most common type. As an example if A is numeric and B is string, to_numpy() will cast the entire array to string, which may not be what you want. Fortunately zipping your columns together is the most straightforward workaround to this.

*Your mileage may vary for the reasons outlined in the Caveats section above.

An Obvious Example

Let"s demonstrate the difference with a simple example of adding two pandas columns A + B. This is a vectorizable operaton, so it will be easy to contrast the performance of the methods discussed above. Benchmarking code, for your reference. The line at the bottom measures a function written in numpandas, a style of Pandas that mixes heavily with NumPy to squeeze out maximum performance. Writing numpandas code should be avoided unless you know what you"re doing. Stick to the API where you can (i.e., prefer vec over vec_numpy).

I should mention, however, that it isn"t always this cut and dry. Sometimes the answer to "what is the best method for an operation" is "it depends on your data". My advice is to test out different approaches on your data before settling on one.

* Pandas string methods are "vectorized" in the sense that they are specified on the series but operate on each element. The underlying mechanisms are still iterative, because string operations are inherently hard to vectorize.

A common trend I notice from new users is to ask questions of the form "How can I iterate over my df to do X?". Showing code that calls iterrows() while doing something inside a for loop. Here is why. A new user to the library who has not been introduced to the concept of vectorization will likely envision the code that solves their problem as iterating over their data to do something. Not knowing how to iterate over a DataFrame, the first thing they do is Google it and end up here, at this question. They then see the accepted answer telling them how to, and they close their eyes and run this code without ever first questioning if iteration is not the right thing to do.

The aim of this answer is to help new users understand that iteration is not necessarily the solution to every problem, and that better, faster and more idiomatic solutions could exist, and that it is worth investing time in exploring them. I"m not trying to start a war of iteration vs. vectorization, but I want new users to be informed when developing solutions to their problems with this library.

This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)

There are seven possible actions to take when such event occurs:

• CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.
• PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.
• RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)
• SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.
• SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.
• SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.
• DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)

Source: Django documentation

In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.

It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.

In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:

• "No! Please! Don"t! I can"t live without you!" (which is said PROTECT or RESTRICT in Django/SQL)
• "All right, if I"m not yours, then I"m nobody"s" (which is said SET_NULL)
• "Good bye world, I can"t live without article_B" and commit suicide (this is the CASCADE behavior).
• "It"s OK, I"ve got spare lover, and I"ll reference article_C from now" (SET_DEFAULT, or even SET(...)).
• "I can"t face reality, and I"ll keep calling your name even if that"s the only thing left to me!" (DO_NOTHING)

I hope it makes cascade direction clearer. :)

Footnotes

(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.

(2) Actually, there is one case where DO_NOTHING can be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.

Label vs. Location

The main distinction between the two methods is:

• loc gets rows (and/or columns) with particular labels.

• iloc gets rows (and/or columns) at integer locations.

To demonstrate, consider a series s of characters with a non-monotonic integer index:

>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2])
49    a
48    b
47    c
0     d
1     e
2     f

>>> s.loc    # value at index label 0
"d"

>>> s.iloc   # value at index location 0
"a"

>>> s.loc[0:1]  # rows at index labels between 0 and 1 (inclusive)
0    d
1    e

>>> s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49    a

Here are some of the differences/similarities between s.loc and s.iloc when passed various objects:

<object> description s.loc[<object>] s.iloc[<object>]
0 single item Value at index label 0 (the string "d") Value at index location 0 (the string "a")
0:1 slice Two rows (labels 0 and 1) One row (first row at location 0)
1:47 slice with out-of-bounds end Zero rows (empty Series) Five rows (location 1 onwards)
1:47:-1 slice with negative step three rows (labels 1 back to 47) Zero rows (empty Series)
[2, 0] integer list Two rows with given labels Two rows with given locations
s > "e" Bool series (indicating which values have the property) One row (containing "f") NotImplementedError
(s>"e").values Bool array One row (containing "f") Same as loc
999 int object not in index KeyError IndexError (out of bounds)
-1 int object not in index KeyError Returns last value in s
lambda x: x.index callable applied to series (here returning 3rd item in index) s.loc[s.index] s.iloc[s.index]

loc"s label-querying capabilities extend well-beyond integer indexes and it"s worth highlighting a couple of additional examples.

Here"s a Series where the index contains string objects:

>>> s2 = pd.Series(s.index, index=s.values)
>>> s2
a    49
b    48
c    47
d     0
e     1
f     2

Since loc is label-based, it can fetch the first value in the Series using s2.loc["a"]. It can also slice with non-integer objects:

>>> s2.loc["c":"e"]  # all rows lying between "c" and "e" (inclusive)
c    47
d     0
e     1

For DateTime indexes, we don"t need to pass the exact date/time to fetch by label. For example:

>>> s3 = pd.Series(list("abcde"), pd.date_range("now", periods=5, freq="M"))
>>> s3
2021-01-31 16:41:31.879768    a
2021-02-28 16:41:31.879768    b
2021-03-31 16:41:31.879768    c
2021-04-30 16:41:31.879768    d
2021-05-31 16:41:31.879768    e

Then to fetch the row(s) for March/April 2021 we only need:

>>> s3.loc["2021-03":"2021-04"]
2021-03-31 17:04:30.742316    c
2021-04-30 17:04:30.742316    d

Rows and Columns

loc and iloc work the same way with DataFrames as they do with Series. It"s useful to note that both methods can address columns and rows together.

When given a tuple, the first element is used to index the rows and, if it exists, the second element is used to index the columns.

Consider the DataFrame defined below:

>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24

Then for example:

>>> df.loc["c": , :"z"]  # rows "c" and onwards AND columns up to "z"
x   y   z
c  10  11  12
d  15  16  17
e  20  21  22

>>> df.iloc[:, 3]        # all rows, but only the column at index location 3
a     3
b     8
c    13
d    18
e    23

Sometimes we want to mix label and positional indexing methods for the rows and columns, somehow combining the capabilities of loc and iloc.

For example, consider the following DataFrame. How best to slice the rows up to and including "c" and take the first four columns?

>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24

We can achieve this result using iloc and the help of another method:

>>> df.iloc[:df.index.get_loc("c") + 1, :4]
x   y   z   8
a   0   1   2   3
b   5   6   7   8
c  10  11  12  13

get_loc() is an index method meaning "get the position of the label in this index". Note that since slicing with iloc is exclusive of its endpoint, we must add 1 to this value if we want row "c" as well.

The simplest way to get row counts per group is by calling .size(), which returns a Series:

df.groupby(["col1","col2"]).size()

Usually you want this result as a DataFrame (instead of a Series) so you can do:

df.groupby(["col1", "col2"]).size().reset_index(name="counts")

If you want to find out how to calculate the row counts and other statistics for each group continue reading below.

Detailed example:

Consider the following example dataframe:

In : df
Out:
col1 col2  col3  col4  col5  col6
0    A    B  0.20 -0.61 -0.49  1.49
1    A    B -1.53 -1.01 -0.39  1.82
2    A    B -0.44  0.27  0.72  0.11
3    A    B  0.28 -1.32  0.38  0.18
4    C    D  0.12  0.59  0.81  0.66
5    C    D -0.13 -1.65 -1.64  0.50
6    C    D -1.42 -0.11 -0.18 -0.44
7    E    F -0.00  1.42 -0.26  1.17
8    E    F  0.91 -0.47  1.35 -0.34
9    G    H  1.48 -0.63 -1.14  0.17

First let"s use .size() to get the row counts:

In : df.groupby(["col1", "col2"]).size()
Out:
col1  col2
A     B       4
C     D       3
E     F       2
G     H       1
dtype: int64

Then let"s use .size().reset_index(name="counts") to get the row counts:

In : df.groupby(["col1", "col2"]).size().reset_index(name="counts")
Out:
col1 col2  counts
0    A    B       4
1    C    D       3
2    E    F       2
3    G    H       1

Including results for more statistics

When you want to calculate statistics on grouped data, it usually looks like this:

In : (df
...: .groupby(["col1", "col2"])
...: .agg({
...:     "col3": ["mean", "count"],
...:     "col4": ["median", "min", "count"]
...: }))
Out:
col4                  col3
median   min count      mean count
col1 col2
A    B    -0.810 -1.32     4 -0.372500     4
C    D    -0.110 -1.65     3 -0.476667     3
E    F     0.475 -0.47     2  0.455000     2
G    H    -0.630 -0.63     1  1.480000     1

The result above is a little annoying to deal with because of the nested column labels, and also because row counts are on a per column basis.

To gain more control over the output I usually split the statistics into individual aggregations that I then combine using join. It looks like this:

In : gb = df.groupby(["col1", "col2"])
...: counts = gb.size().to_frame(name="counts")
...: (counts
...:  .join(gb.agg({"col3": "mean"}).rename(columns={"col3": "col3_mean"}))
...:  .join(gb.agg({"col4": "median"}).rename(columns={"col4": "col4_median"}))
...:  .join(gb.agg({"col4": "min"}).rename(columns={"col4": "col4_min"}))
...:  .reset_index()
...: )
...:
Out:
col1 col2  counts  col3_mean  col4_median  col4_min
0    A    B       4  -0.372500       -0.810     -1.32
1    C    D       3  -0.476667       -0.110     -1.65
2    E    F       2   0.455000        0.475     -0.47
3    G    H       1   1.480000       -0.630     -0.63

Footnotes

The code used to generate the test data is shown below:

In : import numpy as np
...: import pandas as pd
...:
...: keys = np.array([
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["C", "D"],
...:         ["C", "D"],
...:         ["C", "D"],
...:         ["E", "F"],
...:         ["E", "F"],
...:         ["G", "H"]
...:         ])
...:
...: df = pd.DataFrame(
...:     np.hstack([keys,np.random.randn(10,4).round(2)]),
...:     columns = ["col1", "col2", "col3", "col4", "col5", "col6"]
...: )
...:
...: df[["col3", "col4", "col5", "col6"]] =
...:     df[["col3", "col4", "col5", "col6"]].astype(float)
...:

Disclaimer:

If some of the columns that you are aggregating have null values, then you really want to be looking at the group row counts as an independent aggregation for each column. Otherwise you may be misled as to how many records are actually being used to calculate things like the mean because pandas will drop NaN entries in the mean calculation without telling you about it.

The idiomatic way to do this with Pandas is to use the .sample method of your dataframe to sample all rows without replacement:

df.sample(frac=1)

The frac keyword argument specifies the fraction of rows to return in the random sample, so frac=1 means return all rows (in random order).

Note: If you wish to shuffle your dataframe in-place and reset the index, you could do e.g.

df = df.sample(frac=1).reset_index(drop=True)

Here, specifying drop=True prevents .reset_index from creating a column containing the old index entries.

Follow-up note: Although it may not look like the above operation is in-place, python/pandas is smart enough not to do another malloc for the shuffled object. That is, even though the reference object has changed (by which I mean id(df_old) is not the same as id(df_new)), the underlying C object is still the same. To show that this is indeed the case, you could run a simple memory profiler:

\$ python3 -m memory_profiler .	est.py
Filename: .	est.py

Line #    Mem usage    Increment   Line Contents
================================================
5     68.5 MiB     68.5 MiB   @profile
6                             def shuffle():
7    847.8 MiB    779.3 MiB       df = pd.DataFrame(np.random.randn(100, 1000000))
8    847.9 MiB      0.1 MiB       df = df.sample(frac=1).reset_index(drop=True)

Placing the legend (bbox_to_anchor)

A legend is positioned inside the bounding box of the axes using the loc argument to plt.legend.
E.g. loc="upper right" places the legend in the upper right corner of the bounding box, which by default extents from (0,0) to (1,1) in axes coordinates (or in bounding box notation (x0,y0, width, height)=(0,0,1,1)).

To place the legend outside of the axes bounding box, one may specify a tuple (x0,y0) of axes coordinates of the lower left corner of the legend.

plt.legend(loc=(1.04,0))

A more versatile approach is to manually specify the bounding box into which the legend should be placed, using the bbox_to_anchor argument. One can restrict oneself to supply only the (x0, y0) part of the bbox. This creates a zero span box, out of which the legend will expand in the direction given by the loc argument. E.g.

plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")

places the legend outside the axes, such that the upper left corner of the legend is at position (1.04,1) in axes coordinates.

Further examples are given below, where additionally the interplay between different arguments like mode and ncols are shown. l2 = plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
l3 = plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
l4 = plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
l5 = plt.legend(bbox_to_anchor=(1,0), loc="lower right",
bbox_transform=fig.transFigure, ncol=3)
l6 = plt.legend(bbox_to_anchor=(0.4,0.8), loc="upper right")

Details about how to interpret the 4-tuple argument to bbox_to_anchor, as in l4, can be found in this question. The mode="expand" expands the legend horizontally inside the bounding box given by the 4-tuple. For a vertically expanded legend, see this question.

Sometimes it may be useful to specify the bounding box in figure coordinates instead of axes coordinates. This is shown in the example l5 from above, where the bbox_transform argument is used to put the legend in the lower left corner of the figure.

Postprocessing

Having placed the legend outside the axes often leads to the undesired situation that it is completely or partially outside the figure canvas.

Solutions to this problem are:

One can adjust the subplot parameters such, that the axes take less space inside the figure (and thereby leave more space to the legend) by using plt.subplots_adjust. E.g.

leaves 30% space on the right-hand side of the figure, where one could place the legend.

• Tight layout
Using plt.tight_layout Allows to automatically adjust the subplot parameters such that the elements in the figure sit tight against the figure edges. Unfortunately, the legend is not taken into account in this automatism, but we can supply a rectangle box that the whole subplots area (including labels) will fit into.

plt.tight_layout(rect=[0,0,0.75,1])

• Saving the figure with bbox_inches = "tight"
The argument bbox_inches = "tight" to plt.savefig can be used to save the figure such that all artist on the canvas (including the legend) are fit into the saved area. If needed, the figure size is automatically adjusted.

plt.savefig("output.png", bbox_inches="tight")

• automatically adjusting the subplot params
A way to automatically adjust the subplot position such that the legend fits inside the canvas without changing the figure size can be found in this answer: Creating figure with exact size and no padding (and legend outside the axes)

Comparison between the cases discussed above: Alternatives

A figure legend

One may use a legend to the figure instead of the axes, matplotlib.figure.Figure.legend. This has become especially useful for matplotlib version >=2.1, where no special arguments are needed

fig.legend(loc=7)

to create a legend for all artists in the different axes of the figure. The legend is placed using the loc argument, similar to how it is placed inside an axes, but in reference to the whole figure - hence it will be outside the axes somewhat automatically. What remains is to adjust the subplots such that there is no overlap between the legend and the axes. Here the point "Adjust the subplot parameters" from above will be helpful. An example:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,2*np.pi)
colors=["#7aa0c4","#ca82e1" ,"#8bcd50","#e18882"]
fig, axes = plt.subplots(ncols=2)
for i in range(4):
axes[i//2].plot(x,np.sin(x+i), color=colors[i],label="y=sin(x+{})".format(i))

fig.legend(loc=7)
fig.tight_layout()
plt.show() Legend inside dedicated subplot axes

An alternative to using bbox_to_anchor would be to place the legend in its dedicated subplot axes (lax). Since the legend subplot should be smaller than the plot, we may use gridspec_kw={"width_ratios":[4,1]} at axes creation. We can hide the axes lax.axis("off") but still put a legend in. The legend handles and labels need to obtained from the real plot via h,l = ax.get_legend_handles_labels(), and can then be supplied to the legend in the lax subplot, lax.legend(h,l). A complete example is below.

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = 6,2

fig, (ax,lax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[4,1]})
ax.plot(x,y, label="y=sin(x)")
....

h,l = ax.get_legend_handles_labels()
lax.axis("off")

plt.tight_layout()
plt.show()

This produces a plot, which is visually pretty similar to the plot from above: We could also use the first axes to place the legend, but use the bbox_transform of the legend axes,

ax.legend(bbox_to_anchor=(0,0,1,1), bbox_transform=lax.transAxes)
lax.axis("off")

In this approach, we do not need to obtain the legend handles externally, but we need to specify the bbox_to_anchor argument.

• Consider the matplotlib legend guide with some examples of other stuff you want to do with legends.
• Some example code for placing legends for pie charts may directly be found in answer to this question: Python - Legend overlaps with the pie chart
• The loc argument can take numbers instead of strings, which make calls shorter, however, they are not very intuitively mapped to each other. Here is the mapping for reference: The fundamental misunderstanding here is in thinking that range is a generator. It"s not. In fact, it"s not any kind of iterator.

You can tell this pretty easily:

>>> a = range(5)
>>> print(list(a))
[0, 1, 2, 3, 4]
>>> print(list(a))
[0, 1, 2, 3, 4]

If it were a generator, iterating it once would exhaust it:

>>> b = my_crappy_range(5)
>>> print(list(b))
[0, 1, 2, 3, 4]
>>> print(list(b))
[]

What range actually is, is a sequence, just like a list. You can even test this:

>>> import collections.abc
>>> isinstance(a, collections.abc.Sequence)
True

This means it has to follow all the rules of being a sequence:

>>> a         # indexable
3
>>> len(a)       # sized
5
>>> 3 in a       # membership
True
>>> reversed(a)  # reversible
<range_iterator at 0x101cd2360>
>>> a.index(3)   # implements "index"
3
>>> a.count(3)   # implements "count"
1

The difference between a range and a list is that a range is a lazy or dynamic sequence; it doesn"t remember all of its values, it just remembers its start, stop, and step, and creates the values on demand on __getitem__.

(As a side note, if you print(iter(a)), you"ll notice that range uses the same listiterator type as list. How does that work? A listiterator doesn"t use anything special about list except for the fact that it provides a C implementation of __getitem__, so it works fine for range too.)

Now, there"s nothing that says that Sequence.__contains__ has to be constant time‚Äîin fact, for obvious examples of sequences like list, it isn"t. But there"s nothing that says it can"t be. And it"s easier to implement range.__contains__ to just check it mathematically ((val - start) % step, but with some extra complexity to deal with negative steps) than to actually generate and test all the values, so why shouldn"t it do it the better way?

But there doesn"t seem to be anything in the language that guarantees this will happen. As Ashwini Chaudhari points out, if you give it a non-integral value, instead of converting to integer and doing the mathematical test, it will fall back to iterating all the values and comparing them one by one. And just because CPython 3.2+ and PyPy 3.x versions happen to contain this optimization, and it"s an obvious good idea and easy to do, there"s no reason that IronPython or NewKickAssPython 3.x couldn"t leave it out. (And in fact, CPython 3.0-3.1 didn"t include it.)

If range actually were a generator, like my_crappy_range, then it wouldn"t make sense to test __contains__ this way, or at least the way it makes sense wouldn"t be obvious. If you"d already iterated the first 3 values, is 1 still in the generator? Should testing for 1 cause it to iterate and consume all the values up to 1 (or up to the first value >= 1)?

Using a for loop, how do I access the loop index, from 1 to 5 in this case?

Use enumerate to get the index with the element as you iterate:

for index, item in enumerate(items):
print(index, item)

And note that Python"s indexes start at zero, so you would get 0 to 4 with the above. If you want the count, 1 to 5, do this:

count = 0 # in case items is empty and you need it after the loop
for count, item in enumerate(items, start=1):
print(count, item)

Unidiomatic control flow

What you are asking for is the Pythonic equivalent of the following, which is the algorithm most programmers of lower-level languages would use:

index = 0            # Python"s indexing starts at zero
for item in items:   # Python"s for loops are a "for each" loop
print(index, item)
index += 1

Or in languages that do not have a for-each loop:

index = 0
while index < len(items):
print(index, items[index])
index += 1

or sometimes more commonly (but unidiomatically) found in Python:

for index in range(len(items)):
print(index, items[index])

Use the Enumerate Function

Python"s enumerate function reduces the visual clutter by hiding the accounting for the indexes, and encapsulating the iterable into another iterable (an enumerate object) that yields a two-item tuple of the index and the item that the original iterable would provide. That looks like this:

for index, item in enumerate(items, start=0):   # default is zero
print(index, item)

This code sample is fairly well the canonical example of the difference between code that is idiomatic of Python and code that is not. Idiomatic code is sophisticated (but not complicated) Python, written in the way that it was intended to be used. Idiomatic code is expected by the designers of the language, which means that usually this code is not just more readable, but also more efficient.

Getting a count

Even if you don"t need indexes as you go, but you need a count of the iterations (sometimes desirable) you can start with 1 and the final number will be your count.

count = 0 # in case items is empty
for count, item in enumerate(items, start=1):   # default is zero
print(item)

print("there were {0} items printed".format(count))

The count seems to be more what you intend to ask for (as opposed to index) when you said you wanted from 1 to 5.

Breaking it down - a step by step explanation

To break these examples down, say we have a list of items that we want to iterate over with an index:

items = ["a", "b", "c", "d", "e"]

Now we pass this iterable to enumerate, creating an enumerate object:

enumerate_object = enumerate(items) # the enumerate object

We can pull the first item out of this iterable that we would get in a loop with the next function:

iteration = next(enumerate_object) # first iteration from enumerate
print(iteration)

And we see we get a tuple of 0, the first index, and "a", the first item:

(0, "a")

we can use what is referred to as "sequence unpacking" to extract the elements from this two-tuple:

index, item = iteration
#   0,  "a" = (0, "a") # essentially this.

and when we inspect index, we find it refers to the first index, 0, and item refers to the first item, "a".

>>> print(index)
0
>>> print(item)
a

Conclusion

• Python indexes start at zero
• To get these indexes from an iterable as you iterate over it, use the enumerate function
• Using enumerate in the idiomatic way (along with tuple unpacking) creates code that is more readable and maintainable:

So do this:

for index, item in enumerate(items, start=0):   # Python indexes start at zero
print(index, item)